Blog
/
Network
/
February 6, 2025

Reimagining Your SOC: Unlocking a Proactive State of Security

Reimagining your SOC Part 3/3: This blog explores the challenges security professionals face in managing cyber risk, evaluates current market solutions, and outlines strategies for building a proactive security posture.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness
Default blog image
06
Feb 2025

Part 1: How to Achieve Proactive Network Security

Part 2: Overcoming Alert Fatigue with AI-Led Investigations  

While the success of a SOC team is often measured through incident management effectiveness (E.g MTTD, MTTR), a true measure of maturity is the reduction of annual security incidents.

Organizations face an increasing number of alerts each year, yet the best SOC teams place focus on proactive operations which don’t reduce the threshold for what becomes an incident but targets the source risks that prevent them entirely.

Freeing up time to focus on cyber risk management is a challenge in and of itself, we cover this in the previous two blogs in this series (see above). However, when the time comes to manage risk, there are several challenges that are unique when compared to detection & response functions within cybersecurity.

Why do cyber risks matter?

While the volume of reported CVEs is increasing at an alarming rate[1], determining the criticality of each vulnerability is becoming increasingly challenging, especially when the likelihood and impact may be different for each organization. Yet vulnerabilities have stood as an important signpost in traditional security and mitigation strategies. Now, without clear prioritization, potentially severe risks may go unreported, leaving organizations exposed to significant threats.

Vulnerabilities also represent just one area of potential risks. Cyberattacks are no longer confined to a single technology type. They now traverse various platforms, including cloud services, email systems, and networks. As technology infrastructure continues to expand, so does the attack surface, making comprehensive visibility across all technology types essential for reducing risk and preventing multi-vector attacks.

However, achieving this visibility is increasingly difficult as infrastructure grows and the cyber risk market remains oversaturated. This visibility challenge extends beyond technology to include personnel and individual cyber hygiene which can still exacerbate broader cyberattacks whether malicious or not.

Organizations must adopt a holistic approach to preventative security. This includes improving visibility across all technology types, addressing human risks, and mobilizing swiftly against emerging security gaps.

“By 2026, 60% of cybersecurity functions will implement business-impact-focused risk assessment methods, aligning cybersecurity strategies with organizational objectives.” [2]

The costs of a fragmented approach

siloed preventative security measures or technologies
Figure 1: Organizations may have a combination of siloed preventative security measures or technologies in place

Unlike other security tools (like SIEM, NDR or SOAR) which contain an established set of capabilities, cyber risk reduction has not traditionally been defined by a single market, rather a variety of products and practices that each provide their own value and are overwhelming if too many are adopted. Just some examples include:

  • Threat and Vulnerability management: Leverages threat intelligence, CVEs and asset management; however, leaves teams with significant patching workflows, ignores business & human factors and is reliant on the speed of teams to keep up with each passing update.  
  • Continuous Controls Monitoring (CCM): Automatically audits the effectiveness of security controls based on industry frameworks but requires careful prioritization and human calculations to set-up effectively. Focuses solely on mobilization.
  • Breach and Attack Simulation (BAS): Automates security posture testing through mock scenarios but require previous prioritization and might not tell you how your specific technologies can be mitigated to reduce that risk.
  • Posture Management technologies: Siloed approaches across Cloud, SaaS, Data Security and even Gen AI that reactively assess misconfigurations and suggest improvements but with only industry frameworks to validate the importance of the risks.
  • Red teaming & Penetration testing: Required by several regulations including (GDPR, HIPPA, PCI, DSS), many organizations hire 'red teams' to perform real breaches in trusted conditions. Penetration tests reveal many flaws, but are not continuous, requiring third-party input and producing long to-do lists with input of broader business risk dependent on the cost of the service.
  • Third-party auditors: Organizations also use third-party auditors to identify assets with vulnerabilities, grade compliance, and recommend improvements. At best, these exercises become tick-box exercises for companies to stay in compliance with the responsibility still on the client to perform further discovery and actioning.

Many of these individual solutions on the market offer simple enhancement, or an automated version of an existing human security task. Ultimately, they lack an understanding of the most critical assets at your organization and are limited in scope, only working in a specific technology area or with the data you provide.

Even when these strategies are complete, implementation of the results require resources, coordination, and buy-in from IT, cybersecurity, and compliance departments. Given the nature of modern business structures, this can be labor and time intensive as responsibilities are shared by organizational segmentation spread across IT, governance, risk and compliance (GRC), and security teams.

Prioritize your true cyber risk with a CTEM approach

Organizations with robust security programs benefit from well-defined policies, standards, key risk indicators (KRIs), and operational metrics, making it easier to measure and report cyber risk accurately.

Implementing a framework like Gartner’s CTEM (Continuous Threat Exposure Management) can help governance by defining the most relevant risks to each organization and which specific solutions meet your improvement needs.

This five-step approach—scoping, discovery, prioritization, validation, and mobilization—encourages focused management cycles, better delegation of responsibilities and a firm emphasis on validating potential risks through technological methods like attack path modeling or breach and attack simulation to add credibility.

Implementing CTEM requires expertise and structure. This begins with an exposure management solution developed uniquely alongside a core threat detection and response offering, to provide visibility of an organization’s most critical risks, whilst linking directly to their incident-based workflows.

“By 2026, organizations prioritizing their security investments, based on a continuous threat exposure management program, will realize a two-third reduction in breaches.” [3]

Achieving a proactive security posture across the whole estate

Unlike conventional tools that focus on isolated risks, Darktrace / Proactive Exposure Management breaks down traditional barriers. Teams can define risk scopes with full, prioritized visibility of the critical risks between: IT/OT networks, email, Active Directory, cloud resources, operational groups, (or even the external attack surface by integrating with Darktrace / Attack Surface Management).

Our innovative, AI-led risk discovery provides a view that mirrors actual attacker methodologies. It does this through advanced algorithms that determine risk based on business importance, rather than traditional device-type prioritization. By implementing a sophisticated damage assessment methodology, security teams don’t just prioritize via severity but instead, the inherent impact, damage, weakness and external exposure of an asset or user.

These calculations also revolutionize vulnerability management by combining industry standard CVE measurements with that organization-specific context to ensure patch management efforts are efficient, rather than an endless list.

Darktrace also integrates MITRE ATT&CK framework mappings to connect all risks through attack path modeling. This offers validation to our AI’s scoring by presenting real world incident scenarios that could occur across your technologies, and the actionable mitigations to mobilize against them.

For those human choke points, security may also deploy targeted phishing engagements. These send real but harmless email ‘attacks’ to test employee susceptibility, strengthening your ability to identify weak points in your security posture, while informing broader governance strategies.

Combining risk with live detection and response

Together, each of these capabilities let teams take the best steps towards reducing risk and the volume of incidents they face. However, getting proactive also sharpens your ability to handle live threats if they occur.  

During real incidents Darktrace users can quickly evaluate the potential impact of affected assets, create their own risk detections based on internal policies, strengthen their autonomous response along critical attack paths, or even see the possible stage of the next attack.

By continually ingesting risk information into live triage workflows, security teams will develop a proactive-first mindset, prioritizing the assets and alerts that have the most impact to the business. This lets them utilize their resource in the most efficient way, freeing up even more time for risk management, mitigation and ensuring continuity for the business.

Whether your organization is laying the foundation for a cybersecurity program or enhancing an advanced one, Darktrace’s self-learning AI adapts to your needs:

  • Foundational stage: For organizations establishing visibility and automating detection and response.
  • Integrated stage: For teams expanding coverage across domains and consolidating tools for simplicity.
  • Proactive stage: For mature security programs enhancing posture with vulnerability management and risk prioritization.

The Darktrace ActiveAI Security Platform empowers security teams to adopt a preventative defense strategy by using Cyber AI Analyst and autonomous response to fuel quicker triage, incident handling and give time back for proactive efforts designed around business impact. The platform encapsulates the critical capabilities that help organizations be proactive and stay ahead of evolving threats.

darktrace proactive exposure management solution brief reduce risk cyber risk

Download the solution brief

Maximize security visibility and reduce risk:

  • Unify risk exposure across all technologies with AI-driven scoring for CVEs, human communications, and architectures.
  • Gain cost and ROI insights on CVE risks, breach costs, patch latency, and blind spots.
  • Strengthen employee awareness with targeted phishing simulations and training.
  • Align proactive and reactive security by assessing device compromises and prevention strategies.
  • Reduce risk with tailored guidance that delivers maximum impact with minimal effort.

Take control of your security posture today. Download here!

References

[1] https://nvd.nist.gov/vuln/search, Search all, Statistics, Total matches By Year 2023 against 2024

[2] https://www.gartner.com/en/documents/5598859

[3] https://www.gartner.com/en/articles/how-to-manage-cybersecurity-threats-not-episodes

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness

More in this series

No items found.

Blog

/

AI

/

July 22, 2026

When AI Agents Go Off Script: What the OpenAI and Hugging Face Incident Means for Defenders

hugging face open aiDefault blog imageDefault blog image

The recent security incident disclosed jointly by OpenAI and Hugging Face is quickly becoming one of the most discussed AI security stories of the year; it's one that has generated great discussion amongst our researchers and technical teams here at Darktrace.

During an internal evaluation of advanced OpenAI models, an autonomous agent reportedly escaped its intended testing boundaries, obtained internet access, and compromised Hugging Face infrastructure while attempting to evaluate and benchmark the model's cyber capabilities. OpenAI has described it as an unprecedented cyber incident and is collaborating with Hugging Face to investigate and share its findings publicly to help defenders understand the implications and improve AI safety across the community.

What this means for organizations deploying AI agents

The most important takeaway for security leaders is not that the AI model is capable of complex and effective chained attacks. It's what this tells us about the guardrails and protections that need to be put in place to ensure these increasingly capable models are safely used within our businesses.

According to OpenAI's investigation, the models were not acting with malicious intent. They were attempting to achieve a legitimate assigned objective. In pursuing that objective, they identified a path that involved obtaining information from a third party's environment and followed it to completion. From the model's perspective, that appears to have been an effective solution to the problem it was given.

Many organizations still hold the assumption that giving an AI agent a legitimate goal will naturally result in legitimate behavior. Increasingly, that is not something defenders can safely assume.

A human who encounters a "permission denied" message generally understands the technical restriction as well as the social meaning behind it. An agent may interpret that message only as evidence that one method to achieve its objective has failed. What makes this incident notable is the agent’s ability to keep pursuing the objective, revise its plan, and combine many individually useful actions into an outcome its designers did not intend.

This is not an isolated challenge. Recent research from the UK's AI Security Institute found that frontier models often attempt to "cheat" evaluations by finding unintended shortcuts to success. More interestingly, that behavior is not always reflected in chain-of-thought outputs and is not always admitted when models are questioned about their actions. One lesson for defenders is that understanding what an AI system says it is doing is not always the same as understanding its behavior and what it is actually doing.

Why behavioral security is an essential security foundation

That's the principle behind Darktrace / SECURE AI and our broader approach to behavioral security. Understanding intent or putting in place static guardrails is not enough; security teams need visibility into how agents behave over time, what resources they interact with, how their behavior changes, and whether they begin pursuing objectives in unexpected ways. Research released by NIST earlier this summer reinforced that rules alone are not sufficient to defend against AI threats, with one conclusion being that "there will always be a way to prompt an AI system to disregard its rules - it's just a matter of finding it."

Another interesting lesson is that nothing described in OpenAI's preliminary findings appears fundamentally impossible for a skilled human attacker. But it does highlight the accessibility and scale that could be unlocked for attackers as increasingly capable models become more widely available. Resource constraints that attackers deal with will decrease as frontier models can help execute elements of those attack workflows autonomously, persist through failure, and iterate towards an objective. Capabilities that were once limited to skilled attackers can increasingly be supplemented with these systems, lowering the barrier to entry to conduct complex cyber operations.

It is incredibly positive to see OpenAI and Hugging Face investigating this incident collaboratively and sharing their findings publicly. Transparency and collaboration around incidents like these helps the entire security community better understand how rapidly AI capabilities are evolving and where we need to improve safeguards. It also reinforces why Darktrace is excited to be partnering with OpenAI through the Daybreak Cyber Partner Program: to explore how advanced AI can be used safely and responsibly in support of defenders while developing the visibility, governance, and security controls needed for increasingly autonomous systems.

This incident is an important reminder that capable AI systems will not always behave in the ways we expect. As organizations deploy AI agents across critical workflows, understanding behavior is an even more important foundation for effective security.

[related-resource]

Continue reading
About the author
Dr. Tim Bazalgette
Chief AI Officer, Darktrace

Blog

/

AI

/

July 22, 2026

How to Secure AI in Stadium Operations?

Photo of a sports stadiumDefault blog imageDefault blog image

Key takeaways

  • AI is entering high-impact stadium functions such as access control, crowd management, ticketing, facilities, and surveillance.  
  • Shadow AI and third-party AI use can create risks that stadium security teams cannot readily see.  
  • Security teams must understand not only which AI systems exist, but also what they can access and what actions they can take.  
  • Live-event resilience requires continuous monitoring and response across AI, IT, OT, identities, and third parties.

Modern stadiums are infrastructure unlike any other. I’ve written before on event day sparking stadiums into life with shops and food stands, transport hubs, vast telecommunications infrastructure, field-side technology and beyond, acting as one super-sized, connected ecosystem. Stadiums’ scale and complexity make them some of the toughest environments in cybersecurity. Now, we’re adding AI to those operations and bringing a new dimension of risk.

The benefits of AI in stadium operations are easy to see. It can help stadium operators move fans safely through crowded gates, forecast demand at concession stands, support biometric entry, identify suspicious behavior on CCTV, and manage heating and ventilation. Used well, it can make live events safer, faster, and more efficient.

But it also changes the security model.

In Darktrace’s recent research into the threat landscape surrounding sports, we asked cybersecurity professionals protecting professional sports organizations where in their footprint a cyber compromise would have the greatest impact. The area they named most, highlighted by 34% of the professionals we spoke to, was stadium operations. At the same time, 35% said their organizations are already using AI in stadium operations, or plan to do so in the next 12 months.

Security teams are no longer just protecting traditional IT systems around a stadium. They are increasingly being asked to protect AI systems that are operating in the stadium’s most fundamental functions.

Approved AI vs. shadow AI in stadium operations

There is a clear difference between AI a stadium’s security team knows about and AI it does not.

Approved AI is the AI that has been reviewed, tested, and integrated into the venue’s operating environment. It may support CCTV analytics, access control, facility management, ticketing, logistics, broadcast operations, or anti-piracy monitoring. It should have clear ownership, access controls, logging, vendor review, and data protection rules. That does not make it risk-free, but it allows security teams to institute proper governance.

Shadow AI is different. It is the unapproved use of AI tools by employees, contractors, or suppliers. It often starts with good intent. Someone wants to work faster. A staff member pastes internal information into a public AI tool to draft a briefing. A developer uses an AI assistant to debug ticketing code. A supplier connects an AI scheduling tool to delivery routes. A designer uploads unreleased venue plans or sponsor material to generate a mockup.

None of those actions may feel like a security decision to the person doing them. But each one can move sensitive operational data into an environment the stadium does not control, creating hidden risk.

The approved AI stack may be visible to security teams. The shadow AI stack often is not.

Why game day increases AI cybersecurity risk

In a typical enterprise environment, a security team may have hours to investigate a strange login or an unexpected connection to a third-party service. Within a stadium, the moment an incident is likely to occur is also the moment when teams are at their most stretched and the incident can have the greatest repercussions: game day.

If an AI system used for crowd management behaves unexpectedly, the issue is not only technical. It may affect physical movement inside the venue.

If a supplier tool is sending operational data to an unapproved AI platform, the issue is not only data governance. It may expose delivery routes, restricted access schedules, or staffing plans.

The most dangerous scenario is not always a loud, dramatic attack but a hidden dependency that no one has mapped such as a vendor adding an AI feature through a software update or a staff workflow using an unapproved tool.

By the time the venue is live, those hidden connections can become operational risk.

The supply chain is part of the stadium attack surface

Any major sporting event is made by its supply chain and partnerships: catering firms, transport providers, broadcast systems, facilities teams. Every piece is necessary and each creates a security channel. The risk of supply chain compromise has been well established for some time and has been the source of some of the most high-profile breaches we’ve seen. The data breach at MSG Entertainment, owner of Madison Square Garden, that was widely reported in March, originated in a breach of Oracle’s E-Business Suite, used in MSG Entertainment’s back-office systems, while the 2018 Olympic Destroyer attack on the Pyeongchang Winter Olympics reportedly began with the compromise of the main IT service provider for the Games. The addition of AI is heightening the risk.

A stadium can have strict rules for its own AI systems, but its vendors may be using separate tools. Some may use AI to manage staffing, delivery windows, inventory, or customer communications. Others may not realize that AI features have been added into software they already use.

This is one of the hardest parts of securing AI in stadium operations. The risk does not always come from a tool the venue selected. It may come from a tool a supplier selected or a feature the supplier did not know had been turned on.

Security teams need to treat vendor AI the same way they treat vendor access. They need to know what suppliers can connect to, what data they can see, what tools they use, and whether those tools introduce new routes for data exposure or lateral movement.

A third-party AI tool does not need deep access to create risk. Sometimes it only needs the right operational detail at the wrong time.

Four questions for securing AI in stadium operations

As AI becomes part of stadium operations, security teams need to move beyond basic approval lists. There are four questions they need to ask:

1. Where is AI being used?

This includes obvious tools, such as computer vision, access control, ticketing, logistics, and facility management. But it also includes less visible AI inside SaaS platforms, vendor tools, browser extensions, developer workflows, smart building systems, and collaboration tools.

2. What can the AI access?

Can it see incident logs, staffing plans, ticketing data, video feeds, building controls, fan information, credentials, or supplier systems? Can it only analyze information, or can it also trigger actions?

3. What can the AI do?

AI agents are not just passive tools. Some can call APIs, update records, generate instructions, trigger workflows, or act with the permissions of a user or service account. In a stadium, that distinction is critical. There is a big difference between an AI system that recommends an action and one that can take an action.

4. What does normal look like?

In your security architecture, static rules will not be enough. AI use changes quickly: tools appear inside existing platforms, vendors add new services, and staff find workarounds when they are under pressure. Security teams need to understand normal behavior across people, identities, devices, networks, cloud services, suppliers, and AI tools so they can spot when something changes.

That is especially important in live-event environments, where small anomalies can matter. A connection to an unapproved AI service may be harmless in one context and serious in another, and an AI agent taking action at 3 a.m. may be expected during setup but suspicious during a match. Context is what turns raw activity into useful security insight. It’s also what enables rapid response. Your own AI-based security systems can respond to threats at machine speed if they can build the live context to know action needs to be taken.

AI can make stadiums safer, but only if it is secured

AI has a real role to play in stadium operations. It can help teams detect crowd pressure earlier, reduce bottlenecks, manage facilities more efficiently, improve the fan experience, and support event teams during high-pressure moments.

The answer is not to slow all AI adoption. That's not the goal. The answer is to make AI visible, governed, and secure before it becomes part of match-day operations.

For stadium operators and event organizers, that means mapping AI use across the venue and supplier ecosystem. It means understanding what each AI system can access and what actions it can take. It means giving staff approved tools that meet their needs, rather than leaving them to find workarounds. It means writing AI use into vendor contracts and audits. And it means monitoring behavior across the full environment, not only the systems that are easiest to see. A stadium cannot secure what it cannot see.

When AI becomes part of how a stadium moves people, controls access, manages facilities, supports suppliers, and protects media rights, it stops being a side project. It becomes part of the event infrastructure.

Event infrastructure must be thoroughly prepared before venue gates open and sustained with the operational resilience required to support a secure, seamless, and reliable event experience.

How Darktrace helps secure AI in stadium operations

Darktrace brings more than a decade of behavioral AI expertise, built on an enterprise‑wide platform designed to operate in complex, ambiguous environments. We protect the large-scale integrated IT and OT environments that underpin stadium operations from the 2022 FIFA World Cup in Qatar, to Formula 1 Grand Prixes around the world and stadiums across the USA.

Other cybersecurity technologies try to predict each new attack based on historical attacks. The problem is that AI operates like humans do. Every action introduces new information that changes how AI behaves, making it unpredictable in nature. Historical attack tactics are now only a small part of the equation, forcing vendors to retrofit unproven acquisitions to secure AI.  

Darktrace is fundamentally different. Our Adaptive AI continuously learns how your people and AI behave, building an understanding of your organization so it can detect and respond autonomously when behavior deviates. Our Behavioral Defense Platform secures your AI, people, and infrastructure as you onboard new workflows, agents, and applications, enabling your AI transformation at scale.

As AI changes what organizations can do, Darktrace helps them move forward with confidence. We give the security teams defending the people and technology within stadium infrastructure the understanding, visibility, and autonomous action they need to protect new technologies as they are integrated into operations, so their organizations drive the progress that will define the AI era.

[related-resource]

Continue reading
About the author
Karim Benslimane
VP, Field CISO
Your data. Our AI.
Elevate your network security with Darktrace AI